High-resolution measurement techniques for distributed and fast soil water dynamics could advance the understanding of subsurface infiltration processes on the plot scale when it can combine high spatial and temporal resolution with a high repeatability of the measured data.Ground-penetrating radar (GPR) is a promising geophysical tool to image and quantify subsurface flow processes in a non-invasive fashion. In the literature, different strategies to collect time-lapse GPR data have been presented. However, so far, no standardized data acquisition and analysis strategy has been established to monitor subsurface changes related to water infiltration and to compare the outcomes of different experiments.Here, we present a 4D-GPR measurement strategy to monitor infiltration experiments by combining an irrigation pad (to simulate moderate rain fall events) with a manually operated 3D GPR measurement platform (equipped with a two-channel GPR antenna array and positioning guides). For investigating the repeatability and resolution limits of our measurement strategy, we conducted a systematic field experiment with two recurrent irrigations at two nearby spots at a selected field plot. Our results show that we can reliably monitor non-uniform subsurface flow processes with a spatial resolution < 5 cm and a temporal resolution below 10 minutes.Because of these so far unreached spatial and temporal resolution capabilities we consider our 4D-GPR measurement strategy as a first step toward a standardized strategy for monitoring infiltration processes. Furthermore, such detailed knowledge about the resolution and repeatability limits of 4D-GPR measurements opens new options for further interpretation approaches, for example without assumptions about a horizontally stratified subsurface model.

Infiltration is a central concern in soil physics. The advective and diffusive redistribution of event water depends on various factors such as initial wetting, the establishment of film connectivity, and capillary gradients and hydraulic conductivity. Non-uniform infiltration patterns are prevalent. However, direct infiltration measurements do not account for this reality and tracer experiments require a destruction of the experimental plot. We developed a data acquisition strategy based on time-lapse 3D ground-penetrating radar (GPR) to monitor fast and small-scale subsurface flow processes during irrigation in a non-invasive manner.The technique combines an irrigation pad (1 m2 drip irrigation to simulate moderate, non-erosive rain events) with a GPR measurement platform (manually driven two-channel GPR antenna array with positioning guides). We will present a systematic field experiment consisting of two recurrent irrigations (40 mm/2 h, 1 irrigation per day) and a respective replicate. For evaluation, the GPR measurements were sidelined with soil moisture measurements (TDR profile) and tracer applications (dye and salt). Our data show that the achieved high resolution of less than 5 cm in space and 10 minutes in time makes it possible to monitor and quantify infiltration processes in their spatial and temporal non-uniformity.The experiment supports the hypothesis from earlier experiments at various sites: Non-uniform infiltration patterns and dynamically connected flow-fields are highly heterogeneous but share stochastic features, such as distribution, randomness, and skewness. Our approach opens new options for repeated, spatially resolved infiltration measurements and theory development for soil hydrology and land surface models.

Ground-penetrating radar (GPR) is a promising geophysical method for investigating subsurface flow processes with high spatial and temporal resolution. While several field examples of GPR monitoring using different measurement strategies have demonstrated its suitability for imaging subsurface processes, only little is known about the data quality and the data repeatability of such experiments. Therefore, we present a systematic field experiment, where we monitor non-uniform infiltration dynamics with repeated 3D GPR measurements (4D GPR). In a detailed analysis of the acquired 4D GPR data, we focus on evaluating data repeatability as well as sources and magnitude of amplitude fluctuations and time shifts not related to subsurface flow processes (i.e., time-lapse noise). Our analyses show that we are able to quantify data repeatability and, thus, to set thresholds for further data interpretation. Demonstrating the application of these thresholds, we perform a first-order, attribute-based interpretation of our 4D GPR data. The results corroborate that we are able to collect temporally and spatially repeatable GPR data and allow us to provide recommendations for performing high-quality GPR monitoring experiments.

Ground-penetrating radar (GPR) full-waveform inversion (FWI) can determine high-resolution electromagnetic properties of the subsurface and has gained increasing attention in near-surface geophysics. However, the application of GPR-FWI to field surface data is limited due to the high computational costs. Here we apply for one of the first times a 2D time-domain gradient-based FWI to synthetic and field multi-offset surface GPR data set. We use subset FWI (SFWI) to reduce computational costs by implementing the simulation on a model subset rather than the whole model. Thus we obtain theoretical speedup and memory saving factor equal to the size ratio of the model to its subset. The properties of the model subset depend on the illumination of the chosen acquisition geometry, based on which we provide rules of thumb for selecting the model subset. Our study reveals that, SFWI can be further improved by parallelizations, where source parallelization allows for higher efficiency than model domain parallelization. Both 2D synthetic and field data validate that SFWI provides results comparable to FWI but without the redundant computations present in FWI. In a field example with surface geometry, SFWI provides a speedup of more than six times for a 45 m survey line. In another field example with crosshole geometry, SFWI achieves more than a three-times acceleration for a 21 m multi-borehole plane. Our study proves that SFWI has the potential for high-performance computation to solve large-scale GPR survey problems.

ABSTRACT Cost‐effective computing capabilities have paved the road for the use of numerical modelling to develop advanced methods and applications of ground‐penetrating radar (GPR). Realistic synthetic data and the corresponding modelling techniques, respectively, should consider all subsurface and above‐ground aspects that influence GPR wave propagation and the characteristics of recorded signals. Critical aspects that can be realized in modern GPR modelling tools include heterogeneous and frequency‐dependent material properties, complex structures and interface geometries as well as three‐dimensional antenna models, including the interaction between the antenna and the subsurface. However, realistic noise related to the electronic components of a GPR system or ambient electromagnetic noise is often not considered, or simplified by assuming a white Gaussian noise model which is added to the modelled data. We present an approach to include realistic noise scenarios as typically observed in GPR field data into the flow of modelling synthetic GPR data. In our approach, we extract the noise from recorded GPR traces and add it to the modelled GPR data via a convolution‐based process. We illustrate our methodology using a modelling exercise, where we contaminate a synthetic two‐dimensional GPR dataset with frequency‐dependent noise recorded in an urban environment. Comparing our noise‐contaminated synthetic data with field data recorded in a similar environment illustrates that our method allows the generation of synthetic GPR with realistic noise characteristics and further highlights the limitations of assuming pure white Gaussian noise models.

SUMMARY Full-waveform inversion (FWI) of ground-penetrating radar (GPR) data has received particular attention in the past decade because it can provide high-resolution subsurface models of dielectric permittivity and electrical conductivity. In most GPR FWIs, these two parameters are regarded as frequency independent, which may lead to false estimates if they strongly depend on frequency, such as in shallow weathered zones. In this study, we develop frequency-dependent GPR FWI to solve this problem. Using the τ-method introduced in the research of viscoelastic waves, we define the permittivity attenuation parameter to quantify the attenuation resulting from the complex permittivity and to modify time-domain Maxwell’s equations. The new equations are self-adjoint so that we can use the same forward engine to back-propagate the adjoint sources and easily derive model gradients in GPR FWI. Frequency dependence analysis shows that permittivity attenuation acts as a low-pass filter, distorting the waveform and decaying the amplitude of the electromagnetic waves. The 2-D synthetic examples illustrate that permittivity attenuation has low sensitivity to the surface multioffset GPR data but is necessary for a good reconstruction of permittivity and conductivity models in frequency-dependent GPR FWI. As a comparison, frequency-independent GPR FWI produces more model artefacts and hardly reconstructs conductivity models dominated by permittivity attenuation. The 2-D field example shows that both FWIs reveal a triangle permittivity anomaly which proves to be a refilled trench. However, frequency-dependent GPR FWI provides a better fit to the observed data and a more robust conductivity reconstruction in a high permittivity attenuation environment. Our GPR FWI results are consistent with previous GPR and shallow-seismic measurements. This research greatly expands the application of GPR FWI in more complicated media.

For a better understanding of hydrological processes in the subsurface, hydrologists need information on different spatial and temporal scales. Geophysical methods provide the opportunity to image and monitor subsurface flow processes beyond point information. In particular, ground-penetrating radar (GPR) is a promising method because it allows for fast data acquisition paired with high spatial resolution. Such characteristics are considered to be critical when monitoring fast temporal and small spatial changes in the subsurface. In this study, we present a field data example of time-lapse GPR reflection measurements to monitor subsurface flow processes induced by an irrigation experiment. We use similarity attributes to analyze our recorded data and to visualize drainage dynamics at a hillslope section in the Ore Mountains, Germany. Our results demonstrate the feasibility of GPR measurements to provide spatiotemporal information on subsurface flow processes at spatial scales of a few decimeters and at temporal scales of a few minutes.

Ground-penetrating radar (GPR) is a method that can provide detailed information about the near subsurface in sedimentary and carbonate environments. The classical interpretation of GPR data (e.g., based on manual feature selection) often is labor-intensive and limited by the experience of the interpreter. Novel attribute-based classification approaches, typically used for seismic interpretation, can provide faster, more repeatable, and less biased interpretations. We have recorded a 3D GPD data set collected across a paleokarst breccia pipe in the Billefjorden area on Spitsbergen, Svalbard. After performing advanced processing, we compare the results of a classical GPR interpretation to the results of an attribute-based classification. Our attribute classification incorporates a selection of dip and textural attributes as the input for a k-means clustering approach. Similar to the results of the classical interpretation, the resulting classes differentiate between undisturbed strata and breccias or fault zones. The classes also reveal details inside the breccia pipe that are not discerned in the classical interpretation. Using nearby outcropping breccia pipes, we infer that the intrapipe GPR facies result from subtle differences, such as breccia lithology, clast size, or pore-space filling.

Proper parameterisation and conceptualisation of the commonplace process of infiltration into the soil is still a topic of debate. Measuring soil water distribution in a spatio-temporally continuous manner can advance our understanding of infiltration in non-uniform flow networks and the soil matrix. At the same time, we find different measurement techniques bound to different concepts and scales, which make a general interpretation and quantification of the data still a challenging task. We present results from several irrigation experiments at the plot and hillslope scale, in which we combined hydrological, geophysical and remote sensing techniques. On this basis, we will point out how different techniques have advantages and pitfalls for their interpretation. E.g. despite the different scales, we found hydraulic conductivity measured in soil cores in good coherence with plot scale experiments, while in-situ measurements with a constant head permeameter deviated substantially. Another example are multispectral data of the changing surface conditions during irrigation which cannot discern different subsurface infiltration patterns, once the surface becomes sufficiently wet. Since any parameterisation links back to the conceptual and numerical models, we have developed an alternative concept to simulate soil water infiltration and redistribution based on a Langangian approach using film flow in representative macropores and a 2D random walk for the soil matrix. Simulations highlight the inherently combined effect of antecedent state and connected preferential flow networks on the respective generation of non-uniform infiltration patterns.

Internal stacking is a common method to reduce incoherent noise and, thus, increases the signal-to-noise ratio (SNR) of GPR data. In this study, we experimentally analyze the signal improvement observed for different stacking values and the applicability of high internal stacking values (>64) when acquiring GPR field data using typical surveying setups. Our results reveal a significant signal improvement up to stacking values of 256, while higher stacking values result only in a slight increase of data quality. Furthermore, we observe limitations in the applicability of very high stacking values (>1,000) for kinematic measurements.

Ground-penetrating radar (GPR) is a standard geophysical technique used to image near-surface structures in sedimentary environments. In such environments, GPR data acquisition and processing are increasingly following 3D strategies. However, the processed GPR data volumes are typically still interpreted using selected 2D slices and manual concepts such as GPR facies analyses. In seismic volume interpretation, the application of (semi-)automated and reproducible approaches such as 3D attribute analyses as well as the production of attribute-based facies models are common practices today. In contrast, the field of 3D GPR attribute analyses and corresponding facies models is largely untapped. We have developed and applied a workflow to produce 3D attribute-based GPR facies models comprising the dominant sedimentary reflection patterns in a GPR volume, which images complex sandy structures on the dune island of Spiekeroog (Northern Germany). After presenting our field site and details regarding our data acquisition and processing, we calculate and filter 3D texture attributes to generate a database comprising the dominant texture features of our GPR data. Then, we perform a dimensionality reduction of this database to obtain meta texture attributes, which we analyze and integrate using composite imaging and (also considering additional geometric information) fuzzy c-means cluster analysis resulting in a classified GPR facies model. Considering our facies model and a corresponding GPR facies chart, we interpret our GPR data set in terms of near-surface sedimentary units, the corresponding depositional environments, and the recent formation history at our field site. Thus, we demonstrate the potential of our workflow, which represents a novel and clear strategy to perform a more objective and consistent interpretation of 3D GPR data collected across different sedimentary environments.

Earth and environmental sciences rely on detailed information about subsurface processes. Whereas geophysical techniques typically provide highly resolved spatial images, monitoring subsurface processes is often associated with enormous effort and, therefore, is usually limited to point information in time or space. Thus, the development of spatial and temporal continuous field monitoring methods is a major challenge for the understanding of subsurface processes. We have developed a novel method for ground-penetrating-radar (GPR) reflection monitoring of subsurface flow processes under unsaturated conditions and applied it to a hydrological infiltration experiment performed across a periglacial slope deposit in northwest Luxembourg. Our approach relies on a spatial and temporal quasicontinuous data recording and processing, followed by an attribute analysis based on analyzing differences between individual time steps. The results demonstrate the ability of time-lapse GPR monitoring to visualize the spatial and temporal dynamics of preferential flow processes with a spatial resolution in the order of a few decimeters and temporal resolution in the order of a few minutes. We observe excellent agreement with water table information originating from different boreholes. This demonstrates the potential of surface-based GPR reflection monitoring to observe the spatiotemporal dynamics of water movements in the subsurface. It provides valuable, and so far not accessible, information for example in the field of hydrology and pedology that allows studying the actual subsurface processes rather than deducing them from point information.

PreviousNext No Access18th International Conference on Ground Penetrating Radar, Golden, Colorado, 14–19 June 2020The redundant wavelet transform to process and interpret GPR dataAuthors: Jens TronickePhilipp KoyanNiklas AllroggenJens TronickeUniversität Potsdam, GermanySearch for more papers by this author, Philipp KoyanUniversität Potsdam, GermanySearch for more papers by this author, and Niklas AllroggenUniversität Potsdam, GermanySearch for more papers by this authorhttps://doi.org/10.1190/gpr2020-104.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract To analyze ground-penetrating radar (GPR) data, we propose a multi-scale decomposition approach based on a redundant wavelet transform (RWT). Our RWT is based on B3-spline filters and the à trous algorithm, which allows to efficiently decompose 1D, 2D, and 3D data with a series of 1D convolutions. Using different examples, we demonstrate potential applications of this approach for data processing and interpretation. Our results show that the RWT is a powerful and computationally efficient tool to improve GPR data analysis. Keywords: GPR, reflection, sedimentology, processing, algorithmPermalink: https://doi.org/10.1190/gpr2020-104.1FiguresReferencesRelatedDetailsCited byRIMFAX dip attribute analysis: Unconformity detection and true dip in the Martian subsurfaceSigurd Eide, Henning Dypvik, Hans Amundsen, David Page, and Svein-Erik Hamran13 October 20223D classified GPR facies models from multi-frequency data volumes: A synthetic studyPhilipp Koyan and Jens Tronicke13 October 20223D ground-penetrating radar attributes to generate classified facies models: A case study from a dune islandPhilipp Koyan, Jens Tronicke, and Niklas Allroggen23 September 2021 | GEOPHYSICS, Vol. 86, No. 6 18th International Conference on Ground Penetrating Radar, Golden, Colorado, 14–19 June 2020ISSN (online):2159-6832Copyright: 2020 Pages: 455 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 11 Nov 2020 CITATION INFORMATION Jens Tronicke, Philipp Koyan, and Niklas Allroggen, (2020), "The redundant wavelet transform to process and interpret GPR data," SEG Global Meeting Abstracts : 400-403. https://doi.org/10.1190/gpr2020-104.1 Plain-Language Summary KeywordsGPRreflectionsedimentologyprocessingalgorithmPDF DownloadLoading ...

In near-surface geophysics, ground-based mapping surveys are routinely employed in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. The resulting geophysical anomaly maps of, for example, magnetic or electrical parameters are usually interpreted to laterally delineate subsurface structures such as those related to the remains of past human activities, subsurface utilities and other installations, hydrological properties, or different soil types. To ease the interpretation of such data sets, we propose a multi-scale processing, analysis, and visualization strategy. Our approach relies on a discrete redundant wavelet transform (RWT) implemented using cubic-spline filters and the à trous algorithm, which allows to efficiently compute a multi-scale decomposition of 2D data using a series of 1D convolutions. The basic idea of the approach is presented using a synthetic test image, while our archaeo-geophysical case study from North-East Germany demonstrates its potential to analyze and process rather typical geophysical anomaly maps including magnetic and topographic data. Our vertical-gradient magnetic data show amplitude variations over several orders of magnitude, complex anomaly patterns at various spatial scales, and typical noise patterns, while our topographic data show a distinct hill structure superimposed by a microtopographic stripe pattern and random noise. Our results demonstrate that the RWT approach is capable to successfully separate these components and that selected wavelet planes can be scaled and combined so that the reconstructed images allow for a detailed, multi-scale structural interpretation also using integrated visualizations of magnetic and topographic data. Because our analysis approach is straightforward to implement without laborious parameter testing and tuning, computationally efficient, and easily adaptable to other geophysical data sets, we believe that it can help to rapidly analyze and interpret different geophysical mapping data collected to address a variety of near-surface applications from engineering practice and research.

PreviousNext No Access18th International Conference on Ground Penetrating Radar, Golden, Colorado, 14–19 June 2020Ground-penetrating radar surveying using antennas with different dominant frequenciesAuthors: Niklas AllroggenJens TronickeNiklas AllroggenUniversität Potsdam, GermanySearch for more papers by this author and Jens TronickeUniversität Potsdam, GermanySearch for more papers by this authorhttps://doi.org/10.1190/gpr2020-083.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Ground-penetrating radar (GPR) array systems make it possible to acquire GPR data simultaneously using antennas with different dominant frequencies. We use a field data example to analyze GPR profiles acquired with different combinations of shielded 250 MHz and 500 MHz antennas. Furthermore, we compare the amplitude spectra of antenna configurations with the same transmitting and receiving antenna (isofrequency configuration) to the amplitude spectra of data collected with different transmitting and receiving antennas (allofrequency configuration). Our results suggest that an allofrequency antenna configuration provides a data set with an intermediate amplitude and frequency response. A combination of a high frequency transmitter and a low frequency receiver provides higher frequency content compared to the flipped configuration and, therefore, is considered as the preferable allofrequency antenna setup. Keywords: electromagnetic, ground penetrating radar, sampling, resolutionPermalink: https://doi.org/10.1190/gpr2020-083.1FiguresReferencesRelatedDetails 18th International Conference on Ground Penetrating Radar, Golden, Colorado, 14–19 June 2020ISSN (online):2159-6832Copyright: 2020 Pages: 455 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 11 Nov 2020 CITATION INFORMATION Niklas Allroggen and Jens Tronicke, (2020), "Ground-penetrating radar surveying using antennas with different dominant frequencies," SEG Global Meeting Abstracts : 316-319. https://doi.org/10.1190/gpr2020-083.1 Plain-Language Summary Keywordselectromagneticground penetrating radarsamplingresolutionPDF DownloadLoading ...

This dataset comprises data of an interdisciplinary pedon-scale irrigation experiment at a grassland site near Karlsruhe, Germany, including pedo-hydrological, geophysical, and remote sensing data. The objective of this experiment is to monitor soil moisture dynamics during a well-defined infiltration process with a combination of direct and non-invasive techniques. Overall, the quantification of soil water dynamics and, in particular, its spatial distributions is essential for the understanding of land-atmosphere interactions. However, the precise measurement of soil water dynamics and its spatial distribution in a continuous manner is a challenging task. Pedo-hydrological monitoring techniques rely on direct, point-based measurement with buried probes for soil water content and matric potential. Non-invasive remote sensing (RS) and geophysical measurement techniques allow for spatially continuous measurements on different spatial scales and extents. This experiment provides a basis for the analyses of signal coherence between the measurement techniques and disciplines. It contributes to forthcoming developments of monitoring setups and modeling approaches to landscape-water dynamics. For direct monitoring, an array of time-domain reflectometry (TDR) probes and tensiometers was used. As non-invasive techniques, we applied a ground-penetrating radar (GPR), a hyperspectral snapshot sensor, a long-wave infrared (LWIR) sensor, and a hyperspectral field spectroradiometer. We provide the data in nearly raw format, including information about the site properties and calibration references. The data are organized along with the different sensors and disciplines. Thus, the distinct sensor data can also be used independently of each other. In addition, exemplary scripts for reading and processing the data are included.

Crosshole ground-penetrating radar (GPR) is applied in areas that require a very detailed subsurface characterization. Analysis of such data typically relies on tomographic inversion approaches providing an image of subsurface parameters. We have developed an approach for processing the reflected energy in crosshole GPR data and applied it on GPR data acquired in different sedimentary settings. Our approach includes muting of the first arrivals, separating the up- and the downgoing wavefield components, and backpropagating the reflected energy by a generalized Kirchhoff migration scheme. We obtain a reflection image that contains information on the location of electromagnetic property contrasts, thus outlining subsurface architecture in the interborehole plane. In combination with velocity models derived from different tomographic approaches, these images allow for a more detailed interpretation of subsurface structures without the need to acquire additional field data. In particular, a combined interpretation of the reflection image and the tomographic velocity model improves the ability to locate layer boundaries and to distinguish different subsurface units. To support our interpretations of our field data examples, we compare our crosshole reflection results with independent information, including borehole logs and surface GPR data.

ABSTRACTGround‐penetrating radar is widely used to provide highly resolved images of subsurface sedimentary structures, with implications for processes active in the vadose zone. Frequently overlooked among these structures are tunnels excavated by fossorial animals (e.g., moles). We present two repeated ground‐penetrating radar surveys performed a year apart in 2016 and 2017. Careful three‐dimensional data processing reveals, in each data set, a pattern of elongated structures that are interpreted as a subsurface mole tunnel network. Our data demonstrate the ability of three‐dimensional ground‐penetrating radar imaging to non‐invasively delineate the small animal tunnels (∼5 cm diameter) at a higher spatial and geolocation resolution than has previously been achieved. In turn, this makes repeated surveys and, therefore, long‐term monitoring possible. Our results offer valuable insight into the understanding of the near‐surface and showcase a potential new application for a geophysical method as well as a non‐invasive method of ecological surveying.